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Detection and Identification of Rare Audiovisual Cues

  • Daphna Weinshall
  • Jörn Anemüller
  • Luc van Gool

Part of the Studies in Computational Intelligence book series (SCI, volume 384)

Table of contents

  1. Front Matter
  2. The DIRAC Project

    1. Front Matter
      Pages 1-1
    2. Jörn Anemüller, Barbara Caputo, Hynek Hermansky, Frank W. Ohl, Tomas Pajdla, Misha Pavel et al.
      Pages 3-35
  3. The Detection of Incongruent Events, Project Survey and Algorithms

    1. Front Matter
      Pages 37-37
    2. Jörg-Hendrik Bach, Hendrik Kayser, Jörn Anemüller
      Pages 39-46
    3. Alon Zweig, Dagan Eshar, Daphna Weinshall
      Pages 47-55
    4. Stefan Kombrink, Mirko Hannemann, Lukáš Burget
      Pages 57-65
    5. Michal Havlena, Jan Heller, Hendrik Kayser, Jörg-Hendrik Bach, Jörn Anemüller, Tomáš Pajdla
      Pages 67-75
    6. Danilo Hollosi, Stefan Wabnik, Stephan Gerlach, Steffen Kortlang
      Pages 77-84
  4. Alternative Frameworks to Detect Meaningful Novel Events

    1. Front Matter
      Pages 85-85
    2. Pau Baiget, Carles Fernández, Xavier Roca, Jordi Gonzàlez
      Pages 87-95
    3. Avishai Hendel, Daphna Weinshall, Shmuel Peleg
      Pages 97-105
  5. Dealing with Meaningful Novel Events, What to Do after Detection

    1. Front Matter
      Pages 107-107
    2. I. Almajai, F. Yan, T. de Campos, A. Khan, W. Christmas, D. Windridge et al.
      Pages 109-117
    3. Tomáš Pajdla, Michal Havlena, Jan Heller
      Pages 119-127
    4. Tatiana Tommasi, Barbara Caputo
      Pages 129-136
  6. How Biological Systems Deal with Novel and Incongruent Events

  7. Back Matter

About this book

Introduction

Machine learning builds models of the world using training data from the application domain and prior knowledge about the problem. The models are later applied to future data in order to estimate the current state of the world. An implied assumption is that the future is stochastically similar to the past. The approach fails when the system encounters situations that are not anticipated from the past experience. In contrast, successful natural organisms identify new unanticipated stimuli and situations and frequently generate appropriate responses.

The observation described above lead to the initiation of the DIRAC EC project in 2006. In 2010 a workshop was held, aimed to bring together researchers and students from different disciplines in order to present and discuss new approaches for identifying and reacting to unexpected events in information-rich environments. This book includes a summary of the achievements of the DIRAC project in chapter 1, and a collection of the papers presented in this workshop in the remaining parts.

Keywords

Computational Intelligence Detection of Rare Audiovisual Cues Identification of Rare Audiovisual Cues Machine Learning

Editors and affiliations

  • Daphna Weinshall
    • 1
  • Jörn Anemüller
    • 2
  • Luc van Gool
    • 3
  1. 1.School of Computer Science and EngineeringHebrew University of Jerusalem JerusalemIsrael
  2. 2.Medical Physics Section, Institute of Physics Carl von Ossietzky UniversityOldenburgGermany
  3. 3.Department Electrical Engineering-ESAT, PSI-VISICSK.U. Leuven HeverleeBelgium

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-24034-8
  • Copyright Information Springer Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-24033-1
  • Online ISBN 978-3-642-24034-8
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site